# CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

arXiv: https://arxiv.org/abs/2110.15794 · HTML: https://mlsanity.com/paper/2110.15794/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Content-Based Citation Recommendation](https://mlsanity.com/paper/1802.08301/) (2018)
- [RecoBERT: A Catalog Language Model for Text-Based Recommendations](https://mlsanity.com/paper/2009.13292/) (2020)

## Built on by (most cited first, 4 of 4)

- [ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting](https://mlsanity.com/paper/2501.06582/) (2025)
- [LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation](https://mlsanity.com/paper/2403.16295/) (2024)
- [Graph-based Keyword Planning for Legal Clause Generation from Topics](https://mlsanity.com/paper/2301.06901/) (2023)
- [Investigating Strategies for Clause Recommendation](https://mlsanity.com/paper/2301.10716/) (2023)

## Abstract (arXiv)

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.